Abstract
The original extreme learning machine (ELM), based on least square solutions, is an efficient learning algorithm used in “generalized” single-hidden layer feedforward networks (SLFNs) which need not be neuron alike. Latest development [1] shows that ELM can be implemented with kernels. Kernlized ELM can be seen as a variant of the conventional LS-SVM without the output bias b. In this paper, the performance comparison of LS-SVM and kernelized ELM is conducted over a benchmarking face recognition dataset. Simulation results show that the kernelized ELM outperforms LS-SVM in terms of both recognition prediction accuracy and training speed.
This research work was sponsored by the grant from Academic Research Fund (AcRF) Tier 1, Ministry of Education, Singapore, under project No. RG22/08(M52040128).
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Zong, W., Zhou, H., Huang, GB., Lin, Z. (2011). Face Recognition Based on Kernelized Extreme Learning Machine. In: Kamel, M., Karray, F., Gueaieb, W., Khamis, A. (eds) Autonomous and Intelligent Systems. AIS 2011. Lecture Notes in Computer Science(), vol 6752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21538-4_26
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